• International Journal of Fuzzy Logic and Intelligent Systems
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• 제14권4호
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• pp.256-267
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• 2014

An automatic switch among ensembles of clustering algorithms is proposed as a part of the bibliographic big data retrieval system by utilizing a fuzzy inference engine as a decision support tool to select the fastest performing clustering algorithm between fuzzy C-means (FCM) clustering, Newman-Girvan clustering, and the combination of both. It aims to realize the best clustering performance with the reduction of computational complexity from O($n^3$) to O(n). The automatic switch is developed by using fuzzy logic controller written in Java and accepts 3 inputs from each clustering result, i.e., number of clusters, number of vertices, and time taken to complete the clustering process. The experimental results on PC (Intel Core i5-3210M at 2.50 GHz) demonstrates that the combination of both clustering algorithms is selected as the best performing algorithm in 20 out of 27 cases with the highest percentage of 83.99%, completed in 161 seconds. The self-adapted FCM is selected as the best performing algorithm in 4 cases and the Newman-Girvan is selected in 3 cases.The automatic switch is to be incorporated into the bibliographic big data retrieval system that focuses on visualization of fuzzy relationship using hybrid approach combining FCM and Newman-Girvan algorithm, and is planning to be released to the public through the Internet.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권5호
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• pp.1886-1908
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• 2020

With the technical advances, the amount of big data is increasing day-by-day such that the traditional software tools face a burden in handling them. Additionally, the presence of the imbalance data in big data is a massive concern to the research industry. In order to assure the effective management of big data and to deal with the imbalanced data, this paper proposes a new indexing algorithm for retrieving big data in the MapReduce framework. In mappers, the data clustering is done based on the Sparse Fuzzy-c-means (Sparse FCM) algorithm. The reducer combines the clusters generated by the mapper and again performs data clustering with the Sparse FCM algorithm. The two-level query matching is performed for determining the requested data. The first level query matching is performed for determining the cluster, and the second level query matching is done for accessing the requested data. The ranking of data is performed using the proposed Monarch chaotic whale optimization algorithm (M-CWOA), which is designed by combining Monarch butterfly optimization (MBO) [22] and chaotic whale optimization algorithm (CWOA) [21]. Here, the Parametric Enabled-Similarity Measure (PESM) is adapted for matching the similarities between two datasets. The proposed M-CWOA outperformed other methods with maximal precision of 0.9237, recall of 0.9371, F1-score of 0.9223, respectively.

• International Journal of Computer Science & Network Security
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• 제21권6호
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• pp.207-212
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• 2021

Clustering Unlabeled Spatial-datasets to convert them to Labeled Spatial-datasets is a challenging task specially for geographical information systems. In this research study we investigated the NYC Taxi Limousine Commission dataset and discover that all of the spatial-temporal trajectory are unlabeled Spatial-datasets, which is in this case it is not suitable for any data mining tasks, such as classification and regression. Therefore, it is necessary to convert unlabeled Spatial-datasets into labeled Spatial-datasets. In this research study we are going to use the Clustering Technique to do this task for all the Trajectory datasets. A key difficulty for applying machine learning classification algorithms for many applications is that they require a lot of labeled datasets. Labeling a Big-data in many cases is a costly process. In this paper, we show the effectiveness of utilizing a Clustering Technique for labeling spatial data that leads to a high-accuracy classifier.

• 인터넷정보학회논문지
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• 제20권1호
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• pp.87-96
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• 2019

본 논문에서는 분산 워크플로우 실행 이벤트 로그를 수집하고 분류하기 위한 사전 처리 도구로서 맵-리듀스기반 클러스터링 기법을 제안한다. 특히 우리는 볼륨, 속도, 다양성, 진실성 및 가치와 같은 BIG 데이터의 5V 속성에 만족하고 잘 충족되어 있기 때문에 분산 워크플로우 실행 이벤트 로그를 특별히 워크플로우 빅-로그(Workflow BIG-Logs)라고 정의한다. 이 논문에서 개발하는 클러스터링 기술은워크플로우 빅-로그를 기반으로 하는 특정 워크플로 프로세스 마이닝 및 분석 알고리즘의 사전 처리 단계에 적용하기 위한 목적으로 고안된 것이다. 즉, 맵리듀스(Map-Reduce) 프레임워크를 워크플로우 빅-로그 처리 플랫폼으로 사용하고, IEEE XES 표준 데이터 형식을 지원하며, 결국 본 연구에서 개발중에 있는 구조적 정보제어넷기반 워크플로우 프로세스 마이닝 알고리즘인 ${\rho}$-알고리즘의 사전 처리 단계 전용으로 사용되도록 구현된 것이다. 보다 자세하게 말하자면, 워크플로우 빅-로그의 클러스터링 패턴은 단위업무액티버티 기반 클러스터링 패턴과 단위업무 수행자 기반 클러스터링 패턴으로 분류되는데, 특별히 단위업무 액티버티 패턴의 하나인 시간적 워크케이스 패턴과 그의 발생 건수를 재발견하는 맵리듀스 기반 클러스터링 알고리즘을 설계하고 구현하고자 한다. 마지막으로, 우리는 BPI 챌린지에서 공개한 워크플로우 실행 이벤트 로그 데이터세트에 대해 일련의 실험을 수행함으로써 제안된 클러스터링 기법의 기술적 타당성을 검증한다.

• 전기학회논문지
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• 제66권5호
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• pp.833-842
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• 2017

In this study, the design methodology of radial basis function neural networks based on incremental K-means clustering is introduced for learning and processing the big data. If there is a lot of dataset to be trained, general clustering may not learn dataset due to the lack of memory capacity. However, the on-line processing of big data could be effectively realized through the parameters operation of recursive least square estimation as well as the sequential operation of incremental clustering algorithm. Radial basis function neural networks consist of condition part, conclusion part and aggregation part. In the condition part, incremental K-means clustering algorithms is used tweights of the conclusion part are given as linear function and parameters are calculated using recursive least squareo get the center points of data and find the fitness using gaussian function as the activation function. Connection s estimation. In the aggregation part, a final output is obtained by center of gravity method. Using machine learning data, performance index are shown and compared with other models. Also, the performance of the incremental K-means clustering based-RBFNNs is carried out by using PSO. This study demonstrates that the proposed model shows the superiority of algorithmic design from the viewpoint of on-line processing for big data.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권7호
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• pp.3714-3732
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• 2019

With the rapid development of network, Intrusion Detection System(IDS) plays a more and more important role in network applications. Many data mining algorithms are used to build IDS. However, due to the advent of big data era, massive data are generated. When dealing with large-scale data sets, most data mining algorithms suffer from a high computational burden which makes IDS much less efficient. To build an efficient IDS over big data, we propose a classification algorithm based on data clustering and data reduction. In the training stage, the training data are divided into clusters with similar size by Mini Batch K-Means algorithm, meanwhile, the center of each cluster is used as its index. Then, we select representative instances for each cluster to perform the task of data reduction and use the clusters that consist of representative instances to build a K-Nearest Neighbor(KNN) detection model. In the detection stage, we sort clusters according to the distances between the test sample and cluster indexes, and obtain k nearest clusters where we find k nearest neighbors. Experimental results show that searching neighbors by cluster indexes reduces the computational complexity significantly, and classification with reduced data of representative instances not only improves the efficiency, but also maintains high accuracy.

• 산업경영시스템학회지
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• 제40권2호
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• pp.92-98
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• 2017

Data clustering determines a group of patterns using similarity measure in a dataset and is one of the most important and difficult technique in data mining. Clustering can be formally considered as a particular kind of NP-hard grouping problem. K-means algorithm which is popular and efficient, is sensitive for initialization and has the possibility to be stuck in local optimum because of hill climbing clustering method. This method is also not computationally feasible in practice, especially for large datasets and large number of clusters. Therefore, we need a robust and efficient clustering algorithm to find the global optimum (not local optimum) especially when much data is collected from many IoT (Internet of Things) devices in these days. The objective of this paper is to propose new Hybrid Simulated Annealing (HSA) which is combined simulated annealing with K-means for non-hierarchical clustering of big data. Simulated annealing (SA) is useful for diversified search in large search space and K-means is useful for converged search in predetermined search space. Our proposed method can balance the intensification and diversification to find the global optimal solution in big data clustering. The performance of HSA is validated using Iris, Wine, Glass, and Vowel UCI machine learning repository datasets comparing to previous studies by experiment and analysis. Our proposed KSAK (K-means+SA+K-means) and SAK (SA+K-means) are better than KSA(K-means+SA), SA, and K-means in our simulations. Our method has significantly improved accuracy and efficiency to find the global optimal data clustering solution for complex, real time, and costly data mining process.

• 정보과학회 컴퓨팅의 실제 논문지
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• 제21권7호
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• pp.494-499
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• 2015

데이터 사이즈가 증가함에 따라서 대용량 데이터를 분석하여 데이터의 특성을 파악하는 것이 매우 중요해졌다. 본 논문에서는 분산 병렬 처리 프레임워크인 맵리듀스를 활용한 k-Means 클러스터링 기반의 효과적인 클러스터링 기법인 MCSK-Means (Multi centroid set k-Means)알고리즘을 제안한다. k-Means 알고리즘은 임의로 정해지는 k개의 초기 중심점들의 위치에 따라서 클러스터링 결과의 정확도가 많은 영향을 받는 문제점을 가지고 있다. 이러한 문제를 해결하기 위하여, 본 논문에서 제안하는 MCSK-Means 알고리즘은 k개의 중심점들로 이루어진 m개의 중심점 집합을 사용하여 임의로 생성되는 초기 중심점의 의존도를 줄였다. 또한, 클러스터링 단계를 거친 m개의 중심점 집합들에 속한 중심점들에 대하여 직접 계층 클러스터링 알고리즘을 적용하여 k개의 클러스터 중심점들을 생성하였다. 본 논문에서는 MCSK-Means 알고리즘을 맵리듀스 프레임워크 환경에서 개발하여 대용량 데이터를 효율적으로 처리할 수 있도록 하였다.

• Communications for Statistical Applications and Methods
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• 제27권6호
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• pp.589-602
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• 2020

The development of smart grids has enabled the easy collection of a large amount of power data. There are some common patterns that make it useful to cluster power consumption patterns when analyzing s power big data. In this paper, clustering analysis is based on distance functions for time series and clustering algorithms to discover patterns for power consumption data. In clustering, we use 10 distance measures to find the clusters that consider the characteristics of time series data. A simulation study is done to compare the distance measures for clustering. Cluster validity measures are also calculated and compared such as error rate, similarity index, Dunn index and silhouette values. Real power consumption data are used for clustering, with five distance measures whose performances are better than others in the simulation.

• Journal of the Korean Data and Information Science Society
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• 제27권3호
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• pp.609-619
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• 2016

본 논문에서는 대용량 데이터를 처리 및 분석하기 위해 RHadoop 플랫폼에서 실제 데이터와 모의 실험 데이터를 가지고 K-평균 클러스터링을 구현하고, MapReduce의 컴바이너 사용여부에 따른 처리 속도를 비교하고자 한다. 또한, K-평균 클러스터링에서 최적의 군집수 결정방법을 MapReduce 프로그램으로 구현하여 실제 데이터에 적용하고자 한다. 그리고 제안된 RHadoop 플랫폼의 확장 가능성을 보이기 위해 실제 데이터에서 R의 기본 패키지에서 kmeans() 함수와 bigmemory 패키지 상에서 유용한 bigkmeans() 함수와 처리 속도를 비교하고자 한다.